clean code
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mszmyd
parent
10c6742b86
commit
3e72d809ec
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numpy==1.26.4
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matplotlib==3.9.0
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125
src/cluster.py
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125
src/cluster.py
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from math import sqrt
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from random import randrange
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from typing import Any, List, Tuple
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import matplotlib.pyplot as plt
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import numpy as np
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class Cluster:
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def __init__(self, centroid: Tuple[Any]):
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self.centroid = centroid
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self.color = np.random.rand(3,)
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self.points = list()
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self.dimension = len(self.centroid)
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if self.dimension != 2 and self.dimension % 3 != 0:
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raise ValueError('dimension must be 2 or a multiple of 3')
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def distance(self, point: Tuple[Any]) -> float:
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sum = 0
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for dim, p in enumerate(self.centroid):
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sum += (p - point[dim]) ** 2
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return sqrt(sum)
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def append(self, point: Tuple[Any]) -> None:
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self.points.append(point)
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def draw(self, draw_centroid: bool = True, *args) -> None:
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if len(self.points) == 0:
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return
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if self.dimension == 2:
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if draw_centroid:
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plt.scatter(*zip(*self.points), c=self.color),
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plt.scatter(
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self.centroid[0], self.centroid[1], c=self.color, edgecolors='k')
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return
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plt.scatter(*zip(*self.points), c=self.color)
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elif self.dimension % 3 == 0:
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unzipped = list(map(list, zip(*self.points)))
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for i, ax in enumerate(args):
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ax.scatter(*unzipped[i * 3: (i + 1) * 3])
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def clear(self) -> None:
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self.points = list()
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def calc_new_centroid(self) -> bool:
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if len(self.points) == 0:
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return False
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arr = np.array(self.points)
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new_centroid = tuple(np.average(arr, axis=0))
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if new_centroid == self.centroid:
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return False
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else:
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self.clear()
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self.centroid = new_centroid
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return True
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def generate_clusters(number_of_clusters: int, number_of_dimensions: int, random_range=0) -> List[Cluster]:
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return [Cluster([randrange(random_range) for _ in range(number_of_dimensions)]) for _ in range(number_of_clusters)]
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def k_means(points: Tuple[Any], clusters: List[Cluster], max_iter: int = 1e9):
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for _ in range(int(max_iter)):
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for point in points:
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ind = 0
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best = 1e7
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for i, cluster in enumerate(clusters):
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d = cluster.distance(point)
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if d < best:
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best = d
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ind = i
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clusters[ind].append(point)
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progress = False
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for cluster in clusters:
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changed = cluster.calc_new_centroid()
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if changed:
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progress = True
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if not progress:
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break
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# TODO: Delete below code
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def load_file(filepath: str):
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points = list()
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with open(filepath, 'r') as f:
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lines = f.readlines()
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for line in lines:
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point = list(map(int, line.split()))
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points.append(point)
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return points
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def main(dimension: str):
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if dimension.lower() == '2d':
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number_of_clusters = 15
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dim = 2
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random_range = int(1e6)
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points = load_file("./s1.txt")
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elif dimension.lower() == '9d':
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number_of_clusters = 2
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dim = 9
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random_range = int(10)
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points = load_file("./breast.txt")
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else:
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raise ValueError('dimension must be 2d or 9d')
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clusters = generate_clusters(number_of_clusters, dim, random_range)
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k_means(points, clusters)
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if dimension.lower() == '9d':
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fig = plt.figure(figsize=(20, 10))
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ax1 = fig.add_subplot(131, projection='3d')
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ax2 = fig.add_subplot(132, projection='3d')
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ax3 = fig.add_subplot(133, projection='3d')
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for cluster in clusters:
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cluster.draw(False, ax1, ax2, ax3)
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else:
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for cluster in clusters:
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cluster.draw()
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plt.show()
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if __name__ == '__main__':
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main('2d')
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# main('9d')
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@ -1,141 +0,0 @@
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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from math import sqrt
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from random import randrange, random
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WORKDIR = './lab1'
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class Cluster:
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def __init__(self, centroid, color):
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self.centroid = centroid
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self.color = color
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self.points = list()
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def distance(self, point):
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sum = 0
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for dim, p in enumerate(self.centroid):
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sum += (p - point[dim]) ** 2
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return sqrt(sum)
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def append(self, point):
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self.points.append(point)
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def draw(self, with_centroids=True, ax1=None, ax2=None, ax3=None):
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if len(self.points) != 0:
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if len(self.points[0]) > 2:
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unzipped = list(map(list, zip(*self.points)))
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ax1.scatter(unzipped[0], unzipped[1], unzipped[2])
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ax2.scatter(unzipped[3], unzipped[4], unzipped[5])
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ax3.scatter(unzipped[6], unzipped[7], unzipped[8])
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else:
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plt.scatter(*zip(*self.points), c=self.color)
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if with_centroids:
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plt.scatter(
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self.centroid[0], self.centroid[1], c=self.color, edgecolors='k')
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def clear(self):
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self.points = []
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def calc_new_centroid(self):
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if len(self.points) == 0:
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return False
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arr = np.array(self.points)
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new_centroid = np.average(arr, axis=0).tolist()
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if new_centroid == self.centroid:
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return False
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else:
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self.clear()
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self.centroid = new_centroid
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return True
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def generate_clusters(number_of_clusters: int, number_of_dimensions: int, random_range=0, with_centroids: bool = True):
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clusters = list()
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if with_centroids:
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for _ in range(number_of_clusters):
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clusters.append(Cluster([randrange(random_range)
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for i in range(number_of_dimensions)], [random(), random(), random()]))
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else:
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for _ in range(number_of_clusters):
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clusters.append(Cluster([-1
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for i in range(number_of_dimensions)], [random(), random(), random()]))
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return clusters
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def k_mean(points, clusters):
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while True:
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for point in points:
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ind = 0
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best = 1e7
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for i, cluster in enumerate(clusters):
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d = cluster.distance(point)
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if d < best:
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best = d
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ind = i
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clusters[ind].append(point)
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progress = False
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for cluster in clusters:
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res = cluster.calc_new_centroid()
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if res:
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progress = True
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if not progress:
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break
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def load_file(filepath: str):
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points = list()
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with open(filepath, 'r') as f:
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lines = f.readlines()
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for line in lines:
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point = list(map(int, line.split()))
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points.append(point)
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return points
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def draw_raw_points(points):
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if len(points[0]) > 2:
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pass
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else:
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plt.scatter(*zip(*points))
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plt.show()
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def main(dimension: str):
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if dimension.lower() == '2d':
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number_of_clusters = 15
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dim = 2
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random_range = int(1e6)
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points = load_file("./s1.txt")
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elif dimension.lower() == '9d':
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number_of_clusters = 2
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dim = 9
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random_range = int(10)
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points = load_file("./breast.txt")
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else:
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raise ValueError('dimension must be 2d or 9d')
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clusters = generate_clusters(number_of_clusters, dim, random_range)
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k_mean(points, clusters)
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if dimension.lower() == '9d':
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fig1 = plt.figure(1)
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ax1 = fig1.add_subplot(projection='3d')
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fig2 = plt.figure(2)
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ax2 = fig2.add_subplot(projection='3d')
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fig3 = plt.figure(3)
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ax3 = fig3.add_subplot(projection='3d')
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for cluster in clusters:
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cluster.draw(ax1, ax2, ax3)
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else:
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for cluster in clusters:
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cluster.draw()
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plt.show()
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if __name__ == '__main__':
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os.chdir(WORKDIR)
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main('2d')
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main('9d')
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